According to the World Health Organization, the global burden of tuberculosis peaked in 2000 and has since declined by 1.5% per year. This modest progress falls short of the Millennial Development Goals for tuberculosis elimination. Moreover, this average decline hides important variation in the incidence and prevalence of tuberculosis around the world today. This variation highlights that determinants of disease incidence may differ across regions, so interventions must be tailored to local the epidemiology of disease. The standard approach to tuberculosis control today relies on detection and treatment of prevalent tuberculosis cases. This approach may have limited effectiveness in areas of high tuberculosis burden where M. tuberculosis transmission is correspondingly high. Indeed, by the time a case is diagnosed and treated, the next generation of cases has already been newly infected. To make progress in curbing the epidemic of tuberculosis, new cases must be prevented, either by reducing transmission or by preventing disease. Household contact investigation has been a mainstay of active case finding of tuberculosis for decades. Although the household is a setting of intense transmission of tuberculosis, household transmission accounts for less than 25% of tuberculosis that occurs in a given community. While the household is a convenient, localized social network for transmission that is easily identified and evaluated, we must better understand the complex community networks that support M. tuberculosis transmission and lead to disease ?hotspots?. The goal of this proposal is to develop methods for measuring community networks that support tuberculosis transmission. In this proposal, we plan to reconstruct the social and mobility networks of infectious tuberculosis cases before diagnosis by using cellular telephone metadata and integrate this information with whole genome sequences of the M. tuberculosis strains from index cases to infer transmission networks. The Specific Aims are: 1) To assess the movement of pulmonary tuberculosis cases before diagnosis as an indicator of M. tuberculosis transmission in an urban African community; 2) To infer transmission trees of M. tuberculosis in an urban African community using whole genome sequencing of isolates from tuberculosis index cases and relate transmission inferences to social networks and mobility patterns. To address these aims, we will expand the design of our current project by collecting archived cellular telephone metadata from index cases for one year prior to the diagnosis of tuberculosis. We will then construct socio-mobility networks that map movement within the community prior to diagnosis of tuberculosis. We will use whole genome sequencing of isolates collected from case networks to reconstruct transmission trees using a Bayesian framework which combines social and mobility networks with phylogenetic information. This novel approach to mapping transmission may give tuberculosis control programs a useful way to tailor public health interventions that are responsive to the local epidemiology of tuberculosis.